OpenRaiser/Intern-Atlas huggingface dataset:
Monocular Human Digitization via Implicit Re-projection Networks
Venue: arXiv cs.CV 2022
Year: 2022

We present an approach to generating 3D human models from images. The key to our framework is that we predict double-sided orthographic depth maps and color images from a single perspective projected image. Our framework consists of three networks. The first network predicts normal maps to recover geometric details such as wrinkles in the clothes and facial regions. The second network predicts shade-removed images for the front and back views by utilizing the predicted normal maps. The last multi-headed network takes both normal maps and shade-free images and predicts depth maps while selectively fusing photometric and geometric information through multi-headed attention gates. Experimental results demonstrate that our method shows visually plausible results and competitive performance in terms of various evaluation metrics over state-of-the-art methods.
l-Leaks: Membership Inference Attacks with Logits
Venue: arXiv cs.LG 2022
Year: 2022

Machine Learning (ML) has made unprecedented progress in the past several decades. However, due to the memorability of the training data, ML is susceptible to various attacks, especially Membership Inference Attacks (MIAs), the objective of which is to infer the model's training data. So far, most of the membership inference attacks against ML classifiers leverage the shadow model with the same structure as the target model. However, empirical results show that these attacks can be easily mitigated if the shadow model is not clear about the network structure of the target model. In this paper, We present attacks based on black-box access to the target model. We name our attack \textbf{l-Leaks}. The l-Leaks follows the intuition that if an established shadow model is similar enough to the target model, then the adversary can leverage the shadow model's information to predict a target sample's membership.The logits of the trained target model contain valuable sample knowledge. We build the shadow model by learning the logits of the target model and making the shadow model more similar to the target model. Then shadow model will have sufficient confidence in the member samples of the target model. We also discuss the effect of the shadow model's different network structures to attack results. Experiments over different networks and datasets demonstrate that both of our attacks achieve strong performance.
Coded Caching at the Edge of Satellite Networks
Venue: arXiv cs.IT 2022
Year: 2022

Caching multimedia contents at the network edge is a key solution to decongest the amount of traffic in the backhaul link. In this paper, we extend and analyze the coded caching technique [1] in an unexplored scenario, i.e. at the edge of two-tier heterogeneous networks with an arbitrary number of users. We characterize the performance of such scheme by deriving a closed-form expression of the average backhaul load and reveal a significant gain compared to other benchmark caching schemes proposed in the literature.
RTMaps-based Local Dynamic Map for multi-ADAS data fusion
Venue: arXiv cs.DB 2022
Year: 2022

Work on Local Dynamic Maps (LDM) implementation is still in its early stages, as the LDM standards only define how information shall be structured in databases, while the mechanism to fuse or link information across different layers is left undefined. A working LDM component, as a real-time database inside the vehicle is an attractive solution to multi-ADAS systems, which may feed a real-time LDM database that serves as a central point of information inside the vehicle, exposing fused and structured information to other components (e.g., decision-making systems). In this paper we describe our approach implementing a real-time LDM component using the RTMaps middleware, as a database deployed in a vehicle, but also at road-side units (RSU), making use of the three pillars that guide a successful fusion strategy: utilisation of standards (with conversions between domains), middlewares to unify multiple ADAS sources, and linkage of data via semantic concepts.
MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework
Venue: arXiv cs.DB 2022
Year: 2022

Supply Chains (SCs) are subject to disruptive events that potentially hinder the operational performance. Disruption Management Process (DMP) relies on the analysis of integrated heterogeneous data sources such as production scheduling, order management and logistics to evaluate the impact of disruptions on the SC. Existing approaches are limited as they address DMP process steps and corresponding data sources in a rather isolated manner which hurdles the systematic handling of a disruption originating anywhere in the SC. Thus, we propose MARE a semantic disruption management and resilience evaluation framework for integration of data sources included in all DMP steps, i.e. Monitor/Model, Assess, Recover and Evaluate. MARE, leverages semantic technologies i.e. ontologies, knowledge graphs and SPARQL queries to model and reproduce SC behavior under disruptive scenarios. Also, MARE includes an evaluation framework to examine the restoration performance of a SC applying various recovery strategies. Semantic SC DMP, put forward by MARE, allows stakeholders to potentially identify the measures to enhance SC integration, increase the resilience of supply networks and ultimately facilitate digitalization.
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems
Venue: arXiv cs.LG 2022
Year: 2022

Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dynamical systems. A prominent instance of such a dynamical system is the system of equations governing fluid dynamics. Recent research results indicate that RL-augmented computational fluid dynamics (CFD) solvers can exceed the current state of the art, for example in the field of turbulence modeling. However, while in supervised learning, the training data can be generated a priori in an offline manner, RL requires constant run-time interaction and data exchange with the CFD solver during training. In order to leverage the potential of RL-enhanced CFD, the interaction between the CFD solver and the RL algorithm thus have to be implemented efficiently on high-performance computing (HPC) hardware. To this end, we present Relexi as a scalable RL framework that bridges the gap between machine learning workflows and modern CFD solvers on HPC systems providing both components with its specialized hardware. Relexi is built with modularity in mind and allows easy integration of various HPC solvers by means of the in-memory data transfer provided by the SmartSim library. Here, we demonstrate that the Relexi framework can scale up to hundreds of parallel environment on thousands of cores. This allows to leverage modern HPC resources to either enable larger problems or faster turnaround times. Finally, we demonstrate the potential of an RL-augmented CFD solver by finding a control strategy for optimal eddy viscosity selection in large eddy simulations.
Precise Change Point Detection using Spectral Drift Detection
Venue: arXiv cs.LG 2022
Year: 2022

The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and need adjustment. In this paper we consider the problem of detecting those change points in unsupervised learning. Many unsupervised approaches rely on the discrepancy between the sample distributions of two time windows. This procedure is noisy for small windows, hence prone to induce false positives and not able to deal with more than one drift event in a window. In this paper we rely on structural properties of drift induced signals, which use spectral properties of kernel embedding of distributions. Based thereon we derive a new unsupervised drift detection algorithm, investigate its mathematical properties, and demonstrate its usefulness in several experiments.
WoLF: the Whole-body Locomotion Framework for Quadruped Robots
Venue: arXiv cs.RO 2022
Year: 2022

The Whole-Body Locomotion Framework (WoLF) is an end-to-end software suite devoted to the loco-manipulation of quadruped robots. WoLF abstracts the complexity of planning and control of quadrupedal robot hardware into a simple to use and robust software that can be connected through multiple tele-operation devices to different quadruped robot models. Furthermore, WoLF allows controlling mounted devices, such as arms or pan-tilt cameras, jointly with the quadrupedal platform. In this short paper, we introduce the main features of WoLF and its overall software architecture.
Convergence Analysis of Deep Residual Networks
Venue: arXiv cs.LG 2022
Year: 2022

Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great usefulness in computer vision by winning the first place in many deep learning competitions. Also, ResNets were the first class of neural networks in the development history of deep learning that are really deep. It is of mathematical interest and practical meaning to understand the convergence of deep ResNets. We aim at characterizing the convergence of deep ResNets as the depth tends to infinity in terms of the parameters of the networks. Toward this purpose, we first give a matrix-vector description of general deep neural networks with shortcut connections and formulate an explicit expression for the networks by using the notions of activation domains and activation matrices. The convergence is then reduced to the convergence of two series involving infinite products of non-square matrices. By studying the two series, we establish a sufficient condition for pointwise convergence of ResNets. Our result is able to give justification for the design of ResNets. We also conduct experiments on benchmark machine learning data to verify our results.
Weakly Supervised Text Classification using Supervision Signals from a Language Model
Venue: arXiv cs.CL 2022
Year: 2022

Solving text classification in a weakly supervised manner is important for real-world applications where human annotations are scarce. In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals. We design a prompt which combines the document itself and "this article is talking about [MASK]." A masked language model can generate words for the [MASK] token. The generated words which summarize the content of a document can be utilized as supervision signals. We propose a latent variable model to learn a word distribution learner which associates generated words to pre-defined categories and a document classifier simultaneously without using any annotated data. Evaluation on three datasets, AGNews, 20Newsgroups, and UCINews, shows that our method can outperform baselines by 2%, 4%, and 3%.
StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map
Venue: arXiv cs.CV 2022
Year: 2022

Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations. To address this problem, we propose a novel framework StyLandGAN, which synthesizes desired landscape images using a depth map which has higher expressive power. Our StyleLandGAN is extended from the unconditional generation model to accept input conditions. We also propose a '2-phase inference' pipeline which generates diverse depth maps and shifts local parts so that it can easily reflect user's intend. As a comparison, we modified the existing semantic image synthesis models to accept a depth map as well. Experimental results show that our method is superior to existing methods in quality, diversity, and depth-accuracy.
Rectangular mesh contour generation algorithm for finite differences calculus
Venue: arXiv cs.CG 2022
Year: 2022

In this work, a 2D contour generation algorithm is proposed for irregular regions. The contour of the physical domain is approximated by mesh segments using the known coordinates of the contour. For this purpose, the algorithm uses a repeating structure that analyzes the known irregular contour coordinates to approximate the physical domain contour by mesh segments. To this end, the algorithm calculates the slope of the line defined by the known point of the irregular contours and the neighboring vertices. In this way, the algorithm calculates the points of the line and its distance to the closest known nodes of the mesh, allowing to obtain the points of the approximate contour. This process is repeated until the approximate contour is obtained. Therefore, this approximate contour generation algorithm, from known nodes of a mesh, is suitable for describing meshes involving geometries with irregular contours and for calculating finite differences in numerical simulations. The contour is evaluated through three geometries, the difference between the areas delimited by the given contour and the approximate contour, the number of nodes and the number of internal points. It can be seen that the increase in geometry complexity implies the need for a greater number of nodes in the contour, generating more refined meshes that allow reaching differences in areas below 2%.
Improved Upper Bound on Independent Domination Number for Hypercubes
Venue: arXiv cs.DM 2022
Year: 2022

We revisit the problem of determining the independent domination number in hypercubes for which the known upper bound is still not tight for general dimensions. We present here a constructive method to build an independent dominating set $S_n$ for the $n$-dimensional hypercube $Q_n$, where $n=2p+1$, $p$ being a positive integer $\ge 1$, provided an independent dominating set $S_p$ for the $p$-dimensional hypercube $Q_p$, is known. The procedure also computes the minimum independent dominating set for all $n=2^k-1$, $k>1$. Finally, we establish that the independent domination number $α_n\leq 3 \times 2^{n-k-2}$ for $7\times 2^{k-2}-1\leq n<2^{k+1}-1$, $k>1$. This is an improved upper bound for this range as compared to earlier work.
MOPaC: The Multiple Offers Protocol for Multilateral Negotiations with Partial Consensus
Venue: arXiv cs.MA 2022
Year: 2022

Existing protocols for multilateral negotiation require a full consensus among the negotiating parties. In contrast, we propose a protocol for multilateral negotiation that allows partial consensus, wherein only a subset of the negotiating parties can reach an agreement. We motivate problems that require such a protocol and describe the protocol formally.
The Capacity of Causal Adversarial Channels
Venue: arXiv cs.IT 2022
Year: 2022

We characterize the capacity for the discrete-time arbitrarily varying channel with discrete inputs, outputs, and states when (a) the encoder and decoder do not share common randomness, (b) the input and state are subject to cost constraints, (c) the transition matrix of the channel is deterministic given the state, and (d) at each time step the adversary can only observe the current and past channel inputs when choosing the state at that time. The achievable strategy involves stochastic encoding together with list decoding and a disambiguation step. The converse uses a two-phase "babble-and-push" strategy where the adversary chooses the state randomly in the first phase, list decodes the output, and then chooses state inputs to symmetrize the channel in the second phase. These results generalize prior work on specific channels models (additive, erasure) to general discrete alphabets and models.
Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture
Venue: arXiv cs.CV 2022
Year: 2022

Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. One popular CNN-based approach involves the application of generated kernels to the input frames to obtain an interpolated frame. Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden. Reducing the size of the model typically impacts performance negatively. This paper presents a method for parameter reduction for a popular flow-less kernel-based network (Adaptive Collaboration of Flows). Through our technique of removing the layers that require the most parameters and replacing them with smaller encoders, we reduce the number of parameters of the network and even achieve better performance compared to the original method. This is achieved by deploying rotation to force each individual encoder to learn different features from the input images. Ablations are conducted to justify design choices and an evaluation on how our method performs on full-length videos is presented.
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose F3AST, an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We show up to 186% and 8% accuracy improvements over FedAvg, and 8% and 7% over FedAdam on CIFAR100 and Shakespeare, respectively.
Who Are We Talking About? Handling Person Names in Speech Translation
Venue: arXiv cs.CL 2022
Year: 2022

Recent work has shown that systems for speech translation (ST) -- similarly to automatic speech recognition (ASR) -- poorly handle person names. This shortcoming does not only lead to errors that can seriously distort the meaning of the input, but also hinders the adoption of such systems in application scenarios (like computer-assisted interpreting) where the translation of named entities, like person names, is crucial. In this paper, we first analyse the outputs of ASR/ST systems to identify the reasons of failures in person name transcription/translation. Besides the frequency in the training data, we pinpoint the nationality of the referred person as a key factor. We then mitigate the problem by creating multilingual models, and further improve our ST systems by forcing them to jointly generate transcripts and translations, prioritising the former over the latter. Overall, our solutions result in a relative improvement in token-level person name accuracy by 47.8% on average for three language pairs (en->es,fr,it).
Codes for Preventing Zeros at Partially Defective Memory Positions
Venue: arXiv cs.IT 2022
Year: 2022

This work deals with error correction for non-volatile memories that are partially defective at some levels. Such memory cells can only store incomplete information since some of their levels cannot be utilized entirely due to, e.g., wearout. On top of that, this paper corrects random errors $t\geq 1$ that could happen among $u$ partially defective cells while preserving their constraints. First, we show that the probability of violating the partially defective cells' restriction due to random errors is not trivial. Next, we update the models in [1] such that the coefficients of the output encoded vector plus the error vector at the partially defective positions are non-zero. Lastly, we state a simple proposition (Proposition 3) for masking the partial defects using a code with a minimum distance $d$ such that $d\geq 2(u+t)+1$. "Masking" means selecting a word whose entries correspond to writable levels in the (partially) defective positions. A comparison shows that masking $u$ cells by this proposition for a particular BCH code is as effective as using the complicated coding scheme proven in [1, Theorem 1].
Physics guided neural networks for modelling of non-linear dynamics
Venue: arXiv cs.LG 2022
Year: 2022

The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. However, it is difficult to train these models on complex dynamical systems from data alone due to their low data efficiency and sensitivity to hyperparameters and initialisation. This work demonstrates that injection of partially known information at an intermediate layer in a DNN can improve model accuracy, reduce model uncertainty, and yield improved convergence during the training. The value of these physics-guided neural networks has been demonstrated by learning the dynamics of a wide variety of nonlinear dynamical systems represented by five well-known equations in nonlinear systems theory: the Lotka-Volterra, Duffing, Van der Pol, Lorenz, and Henon-Heiles systems.
QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels
Venue: arXiv cs.IT 2022
Year: 2022

Providing minimum quality-of-service (QoS) in crowded wireless communications systems, with high user density, is challenging due to the network structure with limited transmit power budget and resource blocks. Smart resource allocation methods, such as user scheduling, power allocation, and modulation and coding scheme selection, must be implemented to cope with the challenge. Aiming to enhance the number of served users with minimum QoS in the downlink (DL) channel of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, in this paper we propose a QoS-aware joint user scheduling and power allocation technique. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints. To accurately evaluate the proposed technique in the XL-MIMO scenario, we propose a generalized non-stationary multi-state channel model based on spherical-wave propagation assuming that users under LoS and NLoS transmission coexist in the same communication cell. Such model considers that users under different channel states experience different propagation aspects both in the multipath fading model and the path loss rule. Numerical results on the achievable sum-rate, number of scheduled users, and distribution of the scheduled users reveal that the proposed CBS algorithm provides a fair coverage over the whole cell area, achieving remarkable numbers of scheduled users when users under the LoS and NLoS channel states coexist in the communication cell.